Growing government standards associated with combustion engine emissions have increased the burden on manufacturers to reduce the amount of nitrogen oxides (NOx) and particulates that may be exhausted from their developed engines. Along with this burden is the manufacturer's commitment to their customers to produce fuel efficient engines. However, the sometimes inverse relationship between fuel economy and reduced emissions tends to make the task of reducing NOx while meeting customer needs a daunting one.
One known type of NOx reduction technique is Selective Catalytic Reduction (SCR). This technique of reducing NOx in a combustion engine generally includes the use of selective reagents, such as ammonia, aqueous urea, and other types of ammonia containing compounds. In a conventional open loop control urea based SCR system, a urea pump may inject a urea solution into the exhaust stream of a combustion engine through an atomizer. An SCR controller may control the rate of urea that is being applied to the atomizer. Within the exhaust stream, the urea solution may decompose into ammonia and carbon dioxide above certain temperatures, such as 160 degrees C. When the exhaust gas mixture is passed over a SCR catalyst, the NOx molecules react with the ammonia molecules over the catalyst sites and are reduced to molecular nitrogen.
The performance of a SCR catalyst to reduce NOx may depend upon may factors, such as catalyst formulation, the size of the catalyst, exhaust gas temperature, and urea dosing rate. With regard to the dosing rate, the NOx reduction efficiency tends to increase linearly until the dosing rate reaches a certain limit. Above the limit, the efficiency of the NOx reduction may start to increase in a slower rate. One reason for the decline in the NOx reduction efficiency is that the ammonia may be supplied at a faster rate that the NOx reduction process can consume. The excess ammonia, known as ammonia slip, may be expelled from the SCR catalyst which may constitute a source of unregulated emissions. A known technique for abating the ammonia slip is to add an oxidation catalyst behind the SCR catalyst. The oxidation catalyst may convert the ammonia back to NOx thus increasing oxide emissions. Accordingly, optimum NOx reduction can be achieved by maximizing the urea dosing rate while avoiding ammonia slip.
A known technique for controlling the urea dosing rate is through an open-loop control process. With the open-loop control process, a predetermined aqueous urea injection rate at a given engine operating load is used for NOx reduction. Due to varying ambient conditions (e.g., humidity, temperature, and pressure), however, the NOx expelled from an engine may vary. Accordingly, an SCR system employing an open-loop control process must allow for a sufficient margin in the urea dosing rate to ensure that NOx emissions standards are met while avoiding ammonia slip. Instituting these margins, however, may lead to under-dosing of the urea solution and loss of NOx reduction performance.
To compensate for the possible loss of performance, SCR systems have implemented closed-loop control processes. In these types of SCR systems, a NOx sensing device, such as a NOx sensor, is placed in the exhaust stream after the SCR catalyst. The sensing device may measure the level of NOx and provide signals to a SCR controller to adjust the urea dosing rate. Although NOx reduction efficiency may be maximized using a closed-loop control process, the costs and maintenance associated with NOx sensing devices make implementing these processes in a SCR system unattractive to engine manufacturers.
To minimize the costs associated with physical sensors, some conventional engine-control systems may implement virtual sensors. U.S. Pat. No. 6,236,908 issued to Cheng et al. shows a vehicle sensor system that stores one or more virtual sensors in the form of neural networks in an Engine Control Module (ECM) of an engine. In the sensor system taught by Cheng et al., the ECM receives values associated with various engine parameters from a plurality of physical sensors and applies various combination of the values to the neural network. Based on these input values, the network then produces values for one or more output parameters. The output values may reflect virtual data that replaces data that would have been received from physical sensors. For example, the neural network may receive various combinations of values from selected physical sensors, such as engine speed, manifold absolute pressure, exhaust gas recirculation, and air/flow ratio values. Based on the input values, the neural network may determine values of other engine operating parameters, including residual mass fraction, emissions, exhaust gas temperatures, and exhaust gas oxygen content. These virtual values may be used by the ECM to control various functions associated with the engine, including spark timing, fuel injection timing, and emissions.
Although Cheng et al. suggests the ability to control emissions through the use of virtual sensors, the control is based on parameters and/or engine functions not directly related to emission characteristics. For example, Cheng et al. teaches generally reducing NOx emissions by lowering peak temperatures during combustion in the engine. The peak temperatures are lowered by controlling various engine functions, such as throttle position to allow more gases to be trapped in a combustion chamber. The engine functions are controlled based on, among other things, a residual mass fraction value determined by a virtual sensor configured on selected input values including engine speed, manifold pressure, exhaust gas recirculation values, and air flow values. Accordingly, the attenuated relationship between emission control functions and selected input and output parameters associated with the virtual sensors taught by Cheng et al. decreases the efficiency and accuracy of the ECM in controlling emissions.